Statistical understanding models applied to dialog applications need a lot of training data. Often, an application needs to support more than one language. This is relevant for countries that have more than one official language. In those applications, users queries convey the same meanings but in different languages. This project presents techniques to automatically deploy statistical comprehension models from a source language to a target language. The goal is to reduce the training data needed and the time requiered to deploy an application in a new language. First, an approach using machine translation techniques is presented. Then, an approach that uses a common semantic space to compare both languages has been developed. Those methods are compared to verify their limits and feasibility. This work present an improvement of the translation model using in-domain data and a novel technique for inferring a multilingual semantic space